جست‌وجوی استاد و پژوهشگر

Haomiao Yuمشاهده پروفایل

استاد

• Microeconomics
• Mathematical Economics
• Game Theory
• +۴ مورد دیگر

Haomiao Yu is a Professor in the Department of Economics at Toronto Metropolitan University, where he serves as Continuing Education Academic Coordinator and Coordinator of the Economics and Management Science Major. His research focuses on microeconomics, mathematical economics, and game theory, with a particular emphasis on large games, equilibrium theory, and strategic interactions under asymmetric information. His work explores foundational topics such as information structures, rationalizability, and the equivalence between individualized and distributionalized games. Recent publications address Pareto refinements, social norms in game theory, and equilibrium existence in non-atomic games. Selected publications highlight his contributions to understanding strategic uncertainty, equilibrium distributions, and the closed-graph property of Nash equilibrium correspondences. His teaching fields align with his research expertise, including microeconomics, game theory, and industrial organization. Contact: haomiao@torontomu.ca | Personal Website | Curriculum Vitae

Rachel Melamedمشاهده پروفایل

استادیار

• Computational Biology
• Cancer Genomics
• Data Science
• +۳ مورد دیگر

Rachel Melamed is an Assistant Professor of Biology at the University of Massachusetts Lowell, affiliated with the Kennedy College of Sciences and the Center of Biomedical and Health Research in Data Sciences (CHORDS). Her research focuses on linking health and molecular data to understand disease mechanisms and identify interventions for conditions like cancer. She employs computational methods from computer science and epidemiology, integrating diverse datasets including health records, biobanks, and drug experiments. Education: BA in Computer Science (Brown University), PhD in Biomedical Informatics (Columbia University), Postdoctoral training in Biomedical Data Science (University of Chicago). Research Interests: Disease biology & genomics, data science, epidemiology, causal inference. Projects include studying drug impacts on cancer pathways, genetic overlaps between diseases, and leveraging big data for personalized medicine. She leads a lab with graduate and undergraduate researchers exploring computational biology, bioinformatics, and pharmacology. Lab Team: Supervises students specializing in computational biology, statistics, and machine learning. Current members include Anugrah Vaishnav, Jianfeng Ke, Panagiotis Lalagkas, and others. Past researchers include Aylinh Eng and Bishoy Sargius. Key Focus Areas: Cancer etiology, drug repurposing, genetic risk stratification, and translational bioinformatics. Her work bridges computational methods with clinical and biological datasets to uncover actionable insights in disease prevention and treatment.

• Computational Semantics
• Natural Language Processing
• Formal Semantics
• +۳ مورد دیگر

Johan Bos is a Professor of Computational Semantics at the University of Groningen. His research focuses on formal semantics, natural language processing, and semantic parsing, with contributions to tools like Boxer and the C&C parser. He leads the Groningen Meaning Bank project, creating multilingual semantic annotations. His work bridges computational linguistics and AI, emphasizing structured meaning representations. Key contributions include foundational textbooks such as Representation and Inference for Natural Language and Learn Prolog Now! . His research spans semantic analysis, discourse representation theory, and cross-lingual meaning parsing. Collaborations include projects on annotated corpora and neural semantic parsing advancements. Publications address challenges in semantic parsing, discourse structures, and interdisciplinary applications like tombstone inscription analysis. He actively contributes to computational semantics conferences and workshops, driving semantic tagging and interlingual meaning representation initiatives.

Eric W. Kleeمشاهده پروفایل

استاد

• Omics Data Analysis
• Rare and Undiagnosed Diseases
• Genomic Medicine
• +۳ مورد دیگر

Eric W. Klee, Ph.D., is a Professor of Biomedical Informatics at Mayo Clinic, leading translational research in omics data integration and precision medicine. He holds key roles including Scientific Director of Research Data and Digital Innovation, Enterprise Co-Leader of Cancer Informatics & Data Science at the Mayo Clinic Comprehensive Cancer Center, and Director of Digital Omics in the Center for Individualized Medicine. His work focuses on rare disease diagnosis, genomic data infrastructure, and machine learning applications in healthcare. Education: Ph.D. in Health Informatics, University of Minnesota MS in Health Informatics, University of Minnesota BS in Electrical Engineering, Iowa State University Research Interests: Dr. Klee’s research integrates multi-omics profiling, AI-driven analytics, and cloud-based platforms to advance diagnostics and treatment for rare genetic disorders. He leads initiatives like RADIaNT (RNA sequencing for rare diseases), SAVI (automated variant interpretation), and RENEW (continuous genomic data reanalysis). His work bridges lab discoveries with clinical practice, emphasizing precision medicine and scalable genomic solutions. Publications Trends: His recent work highlights RNA-based diagnostics, AI in variant prioritization, and infrastructure for population-level genomic screening. Key areas include drug repositioning for tobacco dependence and molecular mechanisms of Mendelian diseases. Awards: Research Award from the Minnesota Partnership for Biotechnology and Medical Genomics (2023) Advising & Grants: Leads the Mayo Clinic’s Digital Omics initiative and co-leads cancer informatics efforts. His lab collaborates on NIH-funded studies and industry partnerships to translate genomic insights into clinical tools. Labs/Teams: Directs the Advanced Diagnostics Laboratory’s bioinformatics team and chairs the Undiagnosed Diseases Network International board, fostering global rare disease research collaboration.

Stefan Leykمشاهده پروفایل

استاد

• GIScience
• Spatial Uncertainty Modeling
• Land Cover Change Modeling
• +۵ مورد دیگر

Stefan Leyk is a Professor of Geography at the University of Colorado Boulder within the Department of Geography in the College of Arts and Sciences. His research focuses on GIScience, spatial uncertainty modeling, and historical landscape analysis, with significant contributions to cartographic pattern recognition from historical maps and spatial dynamic modeling in public health. He holds a Ph.D. from the University of Zurich and the Federal Research Institute for Forest, Snow and Landscape (2005). His primary research interests include uncertainty in GIScience and spatial uncertainty modeling, land cover change modeling using historical spatial information, cartographic pattern recognition from historical maps, and spatial dynamic modeling approaches in public health. His work bridges historical geography with advanced computational methods, particularly in extracting settlement patterns from historical map archives and developing spatiotemporal datasets spanning centuries. Leyk's recent publications demonstrate strong trends in historical settlement analysis, with major projects like CHRONEX-US and HISDAC-US creating century-long datasets of urban infrastructure and settlement evolution. His work increasingly integrates machine learning with historical map processing, focusing on uncertainty quantification, built-up land validation, and environmental justice applications related to flood risk and coastal hazards. Key thematic areas include long-term urban growth patterns, rural poverty dynamics, and wildfire risk assessment at the wildland-urban interface. Leyk has received significant research funding through collaborative grants including 'HNDS-I: Building Long-term, National-scale Spatiotemporal Data Collections from Historical Map Archives' (2025) and 'HNDS-I: Data Infrastructure for Research on Historical Settlement and Population Growth in the United States' (2021). He actively mentors graduate students including Alek Berg, Caitlin McShane, and Yuying Ren, and teaches advanced GIS courses such as GEOG 4303/5303 GIS: Spatial Programming and GEOG 4103/5303 GIS: Spatial Analytics. His laboratory work centers on geospatial modeling of historical settlement and landscape analysis, with a focus on developing automated methods for processing historical map archives and creating linked spatiotemporal data. Current projects involve machine learning applications for feature extraction from historical maps, uncertainty prediction in built-up land layers, and the development of fine-grained datasets measuring 200 years of land development in the United States.

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +۴ مورد دیگر

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

Kiril Gashteovskiمشاهده پروفایل

پژوهشگر

• Open Information Extraction
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Kiril Gashteovski is a PhD candidate at the Chair of Practical Computer Science I: Data Analytics within the School of Business Informatics and Mathematics at the University of Mannheim. He is supervised by Prof. Dr. Rainer Gemulla and affiliated with the Data and Web Science Group. His primary research focuses on open information extraction, natural language processing, and machine learning applications. He concurrently holds a position as Research Scientist at NEC Laboratories Europe GmbH in Heidelberg, Germany. Education: PhD candidate (University of Mannheim, ongoing), prior academic background includes studies and research roles at institutions in Bulgaria, France, and North Macedonia. Research interests emphasize developing efficient open information extraction systems, knowledge base construction, and text analytics applications. His work bridges theoretical advancements with practical implementations in areas like entity linking and corpus development. Publications span topics such as OPIEC corpus development, citation-centered extraction, and toolkit creation for entity aspect linking. He actively presents at venues like AKBC and EMNLP. Advising and supervision include leading team projects like 'Complaint Handling via Text Analytics for Roche' and 'Data analytics on a ticketing system' with Abbvie. He also teaches courses in data mining and machine learning. Labs/Teams: Core member of the Data and Web Science Group at University of Mannheim, collaborating with industry partners like NEC Labs Europe.

Philip R.O. Payneمشاهده پروفایل

استاد

• Artificial Intelligence in Healthcare
• Biomedical Informatics
• Clinical Decision Support Systems
• +۷ مورد دیگر

Philip R.O. Payne holds multiple prestigious roles at Washington University in St. Louis and the BJC Health System. As the Janet and Bernard Becker Professor , he leads the Institute for Informatics, Data Science and Biostatistics (I2DB) . He also serves as Vice Chancellor for Biomedical Informatics and Data Science at WashU Medicine and Chief Health AI Officer for BJC Health System and WashU Medicine. Additionally, he is the founding director of the Center for Health AI , a joint initiative between the two institutions. His academic appointments include professorships in General Internal Medicine and Computer Science and Engineering . Dr. Payne’s research focuses on four key areas: AI-driven biomolecular and clinical phenotype analysis , interventional applications of EHRs and clinical decision support systems , human factors and workflow optimization in healthcare IT , and data sharing and analytics platforms for healthcare systems and research initiatives . His work emphasizes leveraging advanced technologies to enhance healthcare outcomes and operational efficiency, such as developing the M3NetFlow AI model for multi-omic data analysis and the Cumulus federated EHR learning system. In terms of awards, he is an elected fellow of the American College of Medical Informatics (ACMI) American Medical Informatics Association (AMIA) American Institute for Medical and Biological Engineering (AIMBE) International Academy of Health Sciences Informatics (IAHSI) He currently serves as president-elect of AMIA, the leading professional organization in Biomedical and Health Informatics. Dr. Payne’s advising and grants narrative involves leading a research group with over 300 publications and fostering interdisciplinary collaboration. While no formal advisees or grant details are explicitly listed, his entrepreneurial ventures include founding digital health companies and advising health/life sciences organizations and venture capital firms. He emphasizes translating academic innovations into real-world solutions. He is affiliated with key labs/teams like the Institute for Informatics (I2DB) , Center for Health AI , and the Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL) . These teams focus on AI-driven healthcare solutions, data integration, and clinical informatics advancements.

Ellis Patrickمشاهده پروفایل

دانشیار

• Bioinformatics
• Biomedical Data Science
• Statistical Bioinformatics
• +۶ مورد دیگر

Associate Professor Ellis Patrick is an applied statistician and bioinformatician affiliated with the University of Sydney's School of Mathematics and Statistics, the Westmead Institute for Medical Research, and the Sydney Precision Data Science Centre. His research focuses on developing bioinformatics tools to analyze high-dimensional biomedical data, with applications in disease treatment, precision medicine, and understanding molecular drivers of conditions like Alzheimer’s, MS, and cancer. He holds a PhD from the University of Sydney and postdoctoral experience at Harvard Medical School and The Broad Institute. Education: PhD in statistical bioinformatics from the University of Sydney. Postdoctoral training included joint appointments at Brigham and Women’s Hospital, Harvard Medical School, and The Broad Institute of MIT and Harvard. Research Interests: Specializes in statistical bioinformatics, spatial omics, and high-dimensional data analysis, with emphasis on translational research in infectious diseases, organ transplantation, cardiovascular disorders, neurodegeneration, and oncology. His work combines computational methods with clinical data to uncover disease mechanisms and improve therapeutic strategies. Grants & Collaborations: Recent grants include PrecisionGO (MRFF-funded), White matter lesion transcriptomics (National MS Society), and studies on coronary artery disease and graft failure prediction. Collaborates widely with medical researchers, leveraging interdisciplinary teams to tackle complex health challenges. Labs & Teams: Leads the Bioinformatics Cluster at the Sydney Precision Data Science Centre, integrating computational and experimental approaches to advance precision health initiatives.

Ken Barkerمشاهده پروفایل

استاد

• Computer Science
• Data Security
• Distributed Systems
• +۷ مورد دیگر

Ken Barker is a Professor and Director of ISPIA at the University of Calgary's Department of Computer Science within the Faculty of Science. He holds a Ph.D. from the University of Alberta (1990) and has extensive experience in industrial computer systems and database design. His research focuses on distributed systems, data privacy/security, bioinformatics, and heterogeneous systems. He has served as Dean of the Faculty of Science, Department Head of Computer Science, and Past President of the Canadian Association of Computer Science (CACS/AIC). His work emphasizes privacy-preserving technologies, blockchain applications, and event understanding systems. Barker has published over 200 peer-reviewed papers across domains like transaction systems, spatial databases, and astro-informatics. He advocates for balancing data utility with privacy through frameworks like the Privacy Policy Permission Model. His contributions include the CHRONOS event prediction system and blockchain-based platforms for secure data storage.

• Supply Chain Analytics
• Logistics Management
• Operations Research
• +۵ مورد دیگر

Professor Ying Xie is a leading academic at Cranfield University's School of Management, holding the position of Professor of Supply Chain Analytics. Her work bridges engineering, data science, and business management across diverse sectors including maritime logistics, healthcare, and disaster response. Her educational background includes a BEng, MSc, and PhD in Management Science from Coventry University. Key research interests focus on supply chain digitalization and decarbonization , renewable energy supply chain configuration , and AI applications in operational resilience . Her work consistently integrates machine learning with real-world business challenges, particularly in port management and sustainability. Recent publications reveal strong trends in maritime decarbonization (25%), AI-driven supply chain optimization (30%), and healthcare systems resilience (20%). Her research increasingly addresses climate adaptation through circular economy models and clean energy transitions. Principal Fellow of the Higher Education Academy (HEA) Expert reviewer for UKRI MRC, British Academy, and EU Horizon Professor Xie has supervised numerous PhD completions and secured two dedicated scholarships for port management and digital twin research. Her £20m+ grant portfolio includes major projects with EPSRC (£7.6m Net Zero Aviation CDT), Innovate UK (£174k Sustainability Platform), and EU Horizon (£5m MEDiate disaster management). She actively collaborates with the Port of Dover, Ocado Technology, and NHS England. She leads the Co-Innovation Group in the UK National Clean Maritime Research Hub and directs academic activities for the EPSRC Centre for Doctoral Training in Net Zero Aviation, shaping national maritime strategy through the Department for Transport's 'UK Ports the Future' report.

Jonathan Jeschkeمشاهده پروفایل

استاد

• Theoretical Ecology
• Invasion Biology
• Ecological Novelty
• +۶ مورد دیگر

Prof. Dr. Jonathan Jeschke is a Professor at the Department of Biology, Chemistry, Pharmacy of the Free University of Berlin and Head of the Department of Evolutionary and Integrative Ecology at the IGB Berlin. Since 2014, he has held a DFG-funded Heisenberg Professorship in Ecological Novelty. He is a member of the Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB) since 2014 and previously served as Spokesperson for the Aquatic Biodiversity Programme at IGB Berlin (2016–2021). Education: Habilitation in Ecology (2011, LMU Munich & TUM), Doctorate in Ecology (2002, LMU Munich) Research Interests: Theoretical ecology, invasion biology, ecological novelty, evolutionary ecology, predator-prey relationships, biodiversity research, urban ecology, interdisciplinary integration, and research synthesis His research explores the dynamics of biological invasions, particularly focusing on niche shifts, management frameworks, and ecological novelty. He has contributed to the development of classification systems like EICAT+ and SEICAT, and his work bridges invasion ecology with network theory, game design, and citizen science. Recent publications emphasize predictive modeling, climate-driven invasions, and cross-border aquatic species management. Scientific awards include the DFG Heisenberg Professorship (2014–2019). He has served as an editor for Basic and Applied Ecology , Diversity and Distributions , and NeoBiota , among others. His group actively collaborates with international institutions and engages in policy advising for biodiversity protection.

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

• Information Retrieval
• Computational Linguistics
• Knowledge Graphs
• +۵ مورد دیگر

Pei-Chi Lo serves as Assistant Professor in the Department of Information Management at National Sun Yat-sen University (NSYSU), Taiwan, where she leads research at the intersection of information retrieval, computational linguistics, and user profiling. Her work leverages knowledge graphs and large language models to advance contextual understanding systems and social media analysis. Education: PhD in Computer Science, Singapore Management University (supervised by Prof. Ee-Peng Lim) Research Focus: Dr. Lo pioneers knowledge-based information retrieval systems including contextual path generation and knowledge graph reasoning. Her computational linguistics work spans task-specific language models, Singlish (English Creole) processing, and LLM-based knowledge extraction. User profiling research examines behavior-based modeling, adaptive crowdsourcing, and social media personality analysis through community-specific language features. Publication Trends: Her 14 publications (2017-2025) reveal evolving expertise from foundational knowledge graph embeddings to contemporary LLM integration. Recent work (2023-2025) emphasizes causal reasoning with LLMs, judicial document analysis, and temporal knowledge discovery, while maintaining core strengths in contextual retrieval and low-resource language processing. Academic Leadership: Dr. Lo advises 8 Master's students across 2024-2025 cohorts and supervises undergraduate research teams. She has secured multiple competitive grants including NSTC projects on LLM-based causal reasoning (2025-2027) and temporal knowledge discovery (2024-2026), plus institutional funding for sustainable e-commerce and elderly care technology initiatives. Laboratory: Her NSYSU research lab actively recruits students for projects spanning judicial reasoning analysis, personalized travel planning systems, and Singlish processing tools, maintaining strong industry and community engagement through practical NLP applications.

Maribel Acostaمشاهده پروفایل

استاد

• Databases
• Semantic Web
• Artificial Intelligence
• +۳ مورد دیگر

Maribel Acosta is a Professor of Databases and Information Systems at Ruhr-Universität Bochum's Faculty of Computer Science. She holds affiliations with the Institut für Neuroinformatik (INI), focusing on interdisciplinary research combining natural and artificial cognitive systems. Her work spans databases, semantic web technologies, artificial intelligence, and machine learning applied to knowledge graphs. Acosta earned her Ph.D. (Summa Cum Laude, 2017) and Master's in Computer Science from Karlsruhe Institute of Technology (KIT). She has held roles including Junior Professor at RUB (2020–present), Deputy Professor at KIT (2019–2020), and Assistant Lecturer at Heidelberg University (2020). Research Interests: Her primary research focuses on efficient querying of knowledge graphs, integration of machine learning in data management, and semantic web technologies. Key areas include knowledge graph population, federated query processing, and stability in neural networks. Recent work explores applications in automotive systems and social science data integration. Awards: Notable recognitions include a 2022 WWW Best Paper nomination, Aminer's Top-100 Influential Scholars (2020–2021), and multiple teaching excellence awards from KIT (2017–2020). Teaching & Labs: She teaches courses on database systems, knowledge graphs, and artificial intelligence at RUB. Her lab contributes to projects like SMART-KG and has pioneered federated SPARQL query frameworks. She actively supervises doctoral research in knowledge graph applications and machine learning.